candidate-aware decoding 1diffusion language models 1early exit decoding 1speedup 1zero-shot evaluation 1
From the 1 of 3 linked papers with an AI index.
3 papers
cs.CL2026
Commit Locally, Exit Globally: Coordinating Adaptive Sampling and Early Exit in Diffusion Language Models
Chia-Ming Lee, Ming-Ching Chang, Shao-Kai Liu +3
The paper introduces LATCH, a training‑free, candidate‑aware early‑exit framework for diffusion language models that decides when to stop generation and where to accelerate decodin…
cs.CV2026
Partial Ring Scan: Revisiting Scan Order in Vision State Space Models
Yi-Kuan Hsieh, Kuan-Chuan Peng, Xin li +3
State Space Models (SSMs) have emerged as efficient alternatives to attention for vision tasks, offering lineartime sequence processing with competitive accuracy. Vision SSMs, howe…
cs.CV2025
The 9th AI City Challenge
Zheng Tang, Shuo Wang, David C. Anastasiu +25
The ninth AI City Challenge continues to advance real-world applications of computer vision and AI in transportation, industrial automation, and public safety. The 2025 edition fea…